Extended local search algorithm based on nonlinear programming for two-dimensional irregular strip packing problem

Authors:
Stephen C. H. Leung;Yangbin Lin;Defu Zhang
Affiliations:
Department of Management Sciences, City University of Hong Kong, Hong Kong;Department of Computer Science, Xiamen University, Xiamen 361005, China;Department of Computer Science, Xiamen University, Xiamen 361005, China
Venue:
Computers and Operations Research
Year:
2012

Citing 7
Cited 1

Future paths for integer programming and links to artificial intelligence

Computers and Operations Research - Special issue: Applications of integer programming
The irregular cutting-stock problem mdash; a new procedure for deriving the no-fit polygon

Computers and Operations Research
A Review of the Application ofMeta-Heuristic Algorithms to 2D Strip Packing Problems

Artificial Intelligence Review
Incremental Penetration Depth Estimation between Convex Polytopes Using Dual-Space Expansion

IEEE Transactions on Visualization and Computer Graphics
Hybridising Tabu Search with Optimisation Techniques for Irregular Stock Cutting

Management Science
A New Bottom-Left-Fill Heuristic Algorithm for the Two-Dimensional Irregular Packing Problem

Operations Research
An iterated local search algorithm based on nonlinear programming for the irregular strip packing problem

Discrete Optimization

An algorithm for the strip packing problem using collision free region and exact fitting placement

Computer-Aided Design

Quantified Score

Hi-index	0.01

Visualization

Abstract

This paper presents an extended local search algorithm (ELS) for the irregular strip packing problem. It adopts two neighborhoods, swapping two given polygons in a placement and placing one polygon into a new position. The local search algorithm is used to minimize the overlap on the basis of the neighborhoods mentioned above and the unconstrained nonlinear programming model is adopted to further minimize the overlap during the search process. Moreover, the tabu search algorithm is used to avoid local minima, and a compact algorithm is presented to improve the result. The results of standard test instances indicate that when compared with other existing algorithms, the presented algorithm does not only show some signs of competitive power but also updates several best known results.